Abstract: Increased use of nonlinear as well as sensitive loads and devices in the power system, the power quality of the system is becoming more and more crucial. The quality of electric power and disturbances occurred in power signal has become a major issue among the electric power suppliers and customers. The PQ detection and classification are valuable tasks for protection of power system network. Most of the disturbances are non-stationary and transitory in nature hence it requires advanced tools and techniques for the analysis of  PQ disturbances. A number of  PQ events are generated and decomposed using wavelet decomposition algorithm of wavelet transform for accurate detection of disturbances. This dissertation aims at classification of the power quality events. The objective of the study is to classify the various power quality events such as voltage sag, voltage swell, interruptions etc. In this work a new technique is used for categorizing PQ disturbances using wavelet transform and neural network. These process having   gone through   three   main   components. First, to accomplish this task a sample power system will be modeled with suitable simulation software (PSCAD) and the power quality events will be simulated. Second, these simulated events will then be processed with suitable signal processing technique (Wavelet Transform). Third, the features of each of the events will then be extracted for the application of Neural Network. Using an artificial neural network the power quality events will be classified with increased accuracy of classification. The power signal is decomposed by using modified wavelet transform and the classification is carried by using ANN.

Keywords: Power quality, Voltage sag, Voltage swell, Interruption, PSCAD simulation, ANN

| DOI: 10.17148/IJIREEICE.2018.689